Fundamentals of digital image processing
Fundamentals of digital image processing
The perception of multiple objects: a connectionist approach
The perception of multiple objects: a connectionist approach
SCAN: a scalable model of attentional selection
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor planning for 3D object search
Computer Vision and Image Understanding
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation from motion of non-rigid objects by neuronal lateral interaction
Pattern Recognition Letters
Digital Image Processing
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A New Robotics Platform for Neuromorphic Vision: Beobots
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Selective attention for identification model: simulating visual neglect
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Attention links sensing to recognition
Image and Vision Computing
The use of attention and spatial information for rapid facial recognition in video
Image and Vision Computing
Expert Systems with Applications: An International Journal
Towards a Semi-automatic Situation Diagnosis System in Surveillance Tasks
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Attention-from-motion: A factorization approach for detecting attention objects in motion
Computer Vision and Image Understanding
Revisiting Algorithmic Lateral Inhibition and Accumulative Computation
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Computational Agents to Model Knowledge - Theory, and Practice in Visual Surveillance
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Temporal salient graph for sports event detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A proposal for local and global human activities identification
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Optical flow or image subtraction in human detection from infrared camera on mobile robot
Robotics and Autonomous Systems
Review: on the use of agent technology in intelligent, multisensory and distributed surveillance
The Knowledge Engineering Review
Human activity monitoring by local and global finite state machines
Expert Systems with Applications: An International Journal
Mobile robot map building from time-of-flight camera
Expert Systems with Applications: An International Journal
A dynamic saliency attention model based on local complexity
Digital Signal Processing
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A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer's attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user's interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest.